Many automation applications employ motion control systems to control machine position and speed. Such motion control systems typically include one or more motors or similar actuating devices operating under the guidance of a controller, which sends position and speed control instructions to the motor in accordance with a user-defined control algorithm. Some motion control systems operate in a closed-loop configuration, whereby the controller instructs the motor to move to a target position or to transition to a target velocity (a desired state) and receives feedback information indicating an actual state of the motor. The controller monitors the feedback information to determine whether the motor has reached the target position or velocity, and adjusts the control signal to correct errors between the actual state and the desired state.
Designers of motion control systems seek to achieve an optimal trade-off between motion speed and system stability. For example, if the controller commands the motor to transition a mechanical component to a target position at a high torque, the machine may initially close the distance between the current position and the desired position at high speed (and thus in a time-efficient manner), but is likely to overshoot the desired position because of the high torque. Consequently, the controller must apply a corrective signal to bring the machine back to the desired position. It may take several such iterations before the motion system converges on the desired position, resulting in undesired machine oscillations. Conversely, instructing the motor to move at a lower torque may increase the accuracy of the initial state transition and reduce or eliminate machine oscillation, but will increase the amount of time required to place the machine in the desired position. Ideally, the controller gain coefficients should be selected to optimize the trade-off between speed of the state transition and system stability. The process of selecting suitable gain coefficients for the controller is known as tuning.
The response of a controlled mechanical system to a signal from a controller having a given set of controller gain coefficients depends on physical characteristics of the mechanical system, including the inertia and the friction coefficient. The inertia represents the resistance of the motion system to acceleration or deceleration. The friction coefficient represents a friction seen by the motor, such as the friction between the rotor and the shaft. Accurate estimates for the inertia and friction coefficient of a controlled mechanical system can simplify the tuning process and improve performance of the system. However, identifying accurate values for these parameters for a given mechanical system can be difficult. In some cases, the inertia is estimated using manual calculations based on the rated motor data and physical data (weight, dimensions, etc.) of the components comprising the load. Such calculations can be cumbersome and time consuming, and may not yield accurate values for these important parameters.
The above-described is merely intended to provide an overview of some of the challenges facing conventional motion control systems. Other challenges with conventional systems and contrasting benefits of the various non-limiting embodiments described herein may become further apparent upon review of the following description.